Datasets:
Tasks:
Text Generation
Modalities:
Text
Sub-tasks:
language-modeling
Languages:
English
Size:
10K - 100K
ArXiv:
Tags:
question-generation
License:
File size: 1,298 Bytes
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import json
import os
from random import seed, shuffle
import re
from tqdm import tqdm
from typing import Dict
from datasets import load_dataset
SEP_TOKEN = " | "
def create_data(hf_data):
df = hf_data.to_pandas()
output = []
for paragraph, g in df.groupby("paragraph"):
example = {
'paragraph': paragraph.replace(SEP_TOKEN, " "),
'questions': [_g.replace(SEP_TOKEN, " ") for _g in g['question']],
'answers': [_g.replace(SEP_TOKEN, " ") for _g in g['answer']],
}
example["questions_answers"] = SEP_TOKEN.join([f"question: {q}, answer: {a}" for q, a in zip(example["questions"], example["answers"])])
output.append(example)
return output
if __name__ == '__main__':
qg_squad = load_dataset("lmqg/qg_squad")
data_valid = create_data(qg_squad['validation'])
data_train = create_data(qg_squad['train'])
data_test = create_data(qg_squad['test'])
data_all = {'train': data_train, 'validation': data_valid, 'test': data_test}
output = './data/processed'
os.makedirs(output, exist_ok=True)
for k, _data in data_all.items():
with open('{}/{}.jsonl'.format(output, k), 'w') as f:
for single_data in tqdm(_data):
f.write(json.dumps(single_data) + '\n') |